Update generate.py

generate.py

@@ -41,18 +41,18 @@ class GenerateRunner():
 
         # self.save_path = os.path.join('experiments', opt.save_directory, opt.test_file_name,
                                     #   f'evaluation_{opt.epoch}')
-        path = Path(os.path.join(opt.save_directory))
-        path.mkdir(parents=True, exist_ok=True)
-        self.save_path = os.path.join(path)
-        self.exist_flag = Path(f'{self.save_path}/generated_molecules.csv').exists()
-        self.overwrite = opt.overwrite
+        # path = Path(os.path.join(opt.save_directory))
+        # path.mkdir(parents=True, exist_ok=True)
+        # self.save_path = os.path.join(path)
+        # self.exist_flag = Path(f'{self.save_path}/generated_molecules.csv').exists()
+        # self.overwrite = opt.overwrite
         self.dev_no = opt.dev_no
         self.device = torch.device('cpu')
-        global LOG
-        LOG = ul.get_logger(name="generate",
-                            log_path=os.path.join(self.save_path, 'generate.log'))
-        LOG.info(opt)
-        LOG.info("Save directory: {}".format(self.save_path))
+        # global LOG
+        # LOG = ul.get_logger(name="generate",
+        #                     log_path=os.path.join(self.save_path, 'generate.log'))
+        # LOG.info(opt)
+        # LOG.info("Save directory: {}".format(self.save_path))
 
         # Load vocabulary
         with open(os.path.join(opt.vocab_path, 'vocab.pkl'), "rb") as input_file:
@@ -144,7 +144,7 @@ class GenerateRunner():
             data_sorted['Predicted_smi_{}'.format(i + 1)] = sampled_smiles_list[:, i]
 
         result_path = os.path.join(self.save_path, "generated_molecules.csv")
-        LOG.info("Save to {}".format(result_path))
+        # LOG.info("Save to {}".format(result_path))
         data_sorted.to_csv(result_path, index=False)
 
     def sample(self, model_choice, model, src, src_mask, source_length, decode_type, num_samples=10,
@@ -204,8 +204,8 @@ class GenerateRunner():
                 elif model_choice == 'seq2seq':
                     sequences = self.sample_seq2seq(model, mask_current, batch_index_current, decoder_hidden,
                                                     encoder_outputs, max_len, device)
-                else:
-                    LOG.info('Specify transformer or seq2seq for model_choice')
+                # else:
+                #     LOG.info('Specify transformer or seq2seq for model_choice')
 
                 # Check valid and unique
                 smiles = []